Machine Learning

Welcome to the cutting edge of computer programming. A great place to start? Our Machine Learning Crash Course (MLCC). Once you have the basics down, ramp up your skills by applying ML techniques to big datasets in real-world competitions. Other paths take you further into data science, and innovative ML approaches like deep learning and neural networks.

Deep Learning and Neural Networks

Stanford CS231 - CNN for Visual Recognition

Looking to improve your knowledge of convolutional neural networks and computer vision? Try this comprehensive course, put together by a trio of Stanford AI experts. The notes and practice activities are freely available. Python proficiency is required.

About the guide

The guide provides tips and resources to help you develop your technical skills through self-paced, hands-on learning. It is intended for university-level Computer Science students considering seeking an internship or full-time role at Google or in the tech industry generally; and university faculty; and others working in, studying, or curious about software engineering. The resources we’ve cited aren’t meant to replace courses available at your university, but they may help supplement your education or provide an introduction to a topic.

Notices

Following the recommendations or using the resources cited in the guide does not guarantee a job at Google. If you choose to sign in to the guide using a Google account, you are agreeing to Google’s Terms of Service.

Acknowledgements

Much of the information in the guide has been gathered via our work with students, faculty, and universities. In particular, Google would like to express our profound gratitude to our outstanding volunteer faculty advisors: Laleh Behjat, University of Calgary; Judith Gal-Ezer, Open University of Israel; Mia Minnes, University of California San Diego; Sathya Narayanan, California State University Monterey Bay; and S. Monisha Pulimood, The College of New Jersey. They gave substantial input to the design and content, and helped us keep the needs of their faculty peers and students front and center.